7 citations · 9 across the 15 of their papers we have counts for
10 papers · 1 filter
Impact of Layer Norm on Memorization and Generalization in Transformers
Rishi Singhal, Jung-Eun Kim
Layer Normalization (LayerNorm) is one of the fundamental components in transformers that stabilizes training and improves optimization. In recent times, Pre-LayerNorm transformers…
Explaining How Quantization Disparately Skews a Model
Abhimanyu Bellam, Jung-Eun Kim
Post Training Quantization (PTQ) is widely adopted due to its high compression capacity and speed with minimal impact on accuracy. However, we observed that disparate impacts are e…
Severing Spurious Correlations with Data Pruning
Varun Mulchandani, Jung-Eun Kim
Deep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on. Reliance on such correlations can cause these networks…
Representation Magnitude has a Liability to Privacy Vulnerability
Xingli Fang, Jung-Eun Kim
The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions…
Center-Based Relaxed Learning Against Membership Inference Attacks
Xingli Fang, Jung-Eun Kim
Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, t…
Aggregate Representation Measure for Predictive Model Reusability
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim
In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) qu…
ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
Divyang Doshi, Jung-Eun Kim
In this research, we propose an innovative method to boost Knowledge Distillation efficiency without the need for resource-heavy teacher models. Knowledge Distillation trains a sma…
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
Fin Amin, Jung-Eun Kim
When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their input…
Cooperative Learning for Cost-Adaptive Inference
Xingli Fang, Richard Bradford, Jung-Eun Kim
We propose a cooperative training framework for deep neural network architectures that enables the runtime network depths to change to satisfy dynamic computing resource requiremen…
Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim +1
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that prunin…